|Title:||Twist liveliness of spun yarns and the effects on knitted fabric spirality|
|Subject:||Hong Kong Polytechnic University -- Dissertations.|
|Department:||Institute of Textiles and Clothing|
|Pages:||xvii, 215, 24 leaves : ill. (some col.) ; 30 cm.|
|Abstract:||This thesis is concerned with a systematic study of the measurement of yarn twist liveliness and of its quantitative relationship with single jersey fabric spirality. Firstly, investigations were carried out on a methodology and apparatus to be used for evaluating the twist liveliness of spun yarns by the wet snarling method. Optimisation of both the methodology and apparatus was undertaken so that the procedure could be applied with confidence in a standard and practical manner. Examined through intra and inter laboratory studies, it has been shown to produce accurate and repeatable measurements of twist liveliness over a range of 100% cotton ring spun yarn counts from 29.5tex to 84.4tex. As part of any investigations to develop systems that can minimise the residual torque induced in ring spun yarns, it is essential to quantify and accurately evaluate the yarn twist liveliness in a standard manner. The established methodology and apparatus were used to measure twist liveliness of 100% cotton modified Nu-Torque TM singles ring yarn, in comparison with conventional ring yarns. The effects of twist, fibre type and downstream processing on the twist liveliness of the yarns were examined. An analysis of the reduced twist liveliness was carried out in a production trial during which the spinning system was in control and was therefore stable. The effect of twist liveliness on the spirality of single jersey fabrics has long been recognised and spirality has been investigated previously by use of empirical methods. The present study has used, for the first time, an artificial neural network to determine the relationship between the measured twist liveliness of spun yarns and the degree of spirality of pure cotton single jersey fabrics knitted from the yarns. Multiple regression and artificial neural network models for the prediction of the degree of fabric spirality from measured twist liveliness and other contributing parameters were established. It was found that both models have a high ability to predict the amount of fabric spirality although the neural network model produced slightly superior results. The methodology in the study measures twist liveliness by counting the number of snarl turns formed in yarn samples under test. In order to increase the efficiency of the apparatus in use, investigations were conducted with a view to replacing the manual counting of the turns by an automated method using image processing techniques. An image acquisition unit was constructed to obtain images of the yarn samples. Fast Fourier Transform (FFT) and Adaptive Orientated Orthogonal Projective Decomposition (AOP) were used to extract the snarling characteristics and record the number of snarl turns from the captured images. Statistical analyses confirmed that the measurements obtained by the automated method agreed well with the original method of using a twist tester to count the number of snarls for low snarling yarns of medium counts.|
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